Field
This disclosure relates to a spectral data processing apparatus configured to perform principal component analysis of spectral data, and a spectral data processing method.
Description of the Related Art
In the medical field, an observation of biological sample in an enlarged scale using a microscope is widely performed. The biological sample is prepared by slicing a biological tissue. However, since the sample substantially has no color and is transparent, dyeing is performed in many cases.
The biological tissue includes multiple types of substances. Therefore, in order to detect a difference in composition or chemical conditions of these substances, measurement of spectrum (such as ultraviolet spectroscopy, visible spectroscopy, X-ray spectroscopy, Raman spectroscopy, induced Raman spectroscopy, coherent anti-Stokes Raman spectroscopy, infrared absorption spectroscopy, and mass spectrometry) of the biological sample is performed. In particular, an observation method using image information of the biological sample and a spectral apparatus configured to measure the spectrum corresponding thereto is referred to as a spectroscopic imaging method. With this observation method, information such as form, composition, and chemical state of the substances is acquired without dying the biological sample.
As a method for analyzing spectral data including the spectrum, multivariate analysis which treats information on intensity in a wide wavelength region as variable quantities is employed. According to principal component analysis or independent component analysis as a type of the multivariate analysis, even though the spectrum is complicated because vibration spectra or band structures of the respective components included in the biological sample are superimposed, classification or measurement of the chemical state of the biological sample are enabled. Japanese Patent Laid-Open No. 2011-174906 discloses a method of inspecting form information or composition of biological samples by performing principal component analysis of spectrum from pixel to pixel basis, and obtaining distribution of principal component scores.
In observation of the biological sample, observation of a region of a cell size at a high magnification, and observation of a region of a larger tissue size are required. Therefore, in Japanese Patent Laid-Open No. 2011-196853, a region of the tissue size is divided into a plurality of regions (tiles) and enlarged images are acquired for the regions using a microscope with a large magnification. By combining the acquired plurality of enlarged images (tiling), an entire image is created, and the created entire image is observed.
When performing principal component analysis on the spectral data, an eigenvector is applied to the spectrum to obtain a principal component score. Japanese Patent Laid-Open No. 2011-174906 discloses a method of obtaining an eigenvector of a standard sample whose component is known in advance, and using the eigenvector in the principal component analysis on the spectral data. However, when an unknown component is included in the sample, the eigenvector may not be an appropriate vector, and hence the accuracy of the principal component analysis may be insufficient in some cases.
The eigenvector is obtained by solving a high-order such as several tens or hundreds order eigenvalue problem by using a variance-covariance matrix with respect to the spectroscopic spectra of the plurality of pixels which constitute tiles. Therefore, when eigenvectors are obtained for all of the spectral data acquired from each tile, a long time is required for image processing.
From such circumstances, when an attempt is made to acquire the entire image of the sample, it is not easy to obtain appropriate eigenvectors quickly, and there arises a problem that principal component analysis on the spectral data takes a long time.